Montelukast prevents the decrease of interleukin-10 and inhibits NF-κB activation in inflammatory airway of asthmatic guinea pigs
Bibliographic record
Abstract
Interleukin (IL)-10 is an important immunoregulatory and anti-inflammatory cytokine, whereas nuclear factor-kappaB (NF-kappaB) plays an important role in the pathogenesis of asthma. In the present study, the effects of montelukast on the level of IL-10 and on the activation of NF-kappaB in the inflammatory airway of asthmatic guinea pigs were investigated. Guinea pigs were sensitized by ovalbumin. Pulmonary inflammation was observed by hematoxylin and eosin staining. The eosinophils in broncho-alveolar lavage fluid and blood were separated by density gradient centrifugation and counted under microscope. The level of IL-10 in broncho-alveolar lavage fluid was measured by enzyme-linked immunoadsorbent assay. Activation of NF-kappaB in lung tissues was inspected by immunohistochemistry. Montelukast at medium and high doses prevented the decrease of IL-10 in broncho-alveolar lavage fluid (n = 8, p < 0.01 vs. asthma model group), inhibited the activation of NF-kappaB in lung tissues (n = 8; medium dose, p < 0.05; high dose, p < 0.01; vs. asthma model group). There was a significantly negative correlation between the level of IL-10 and the activation of NF-kappaB in lung tissues (r = -0.488, p < 0.01). Montelukast reduced the severity of airway inflammation and the number of eosinophils in asthmatic guinea pigs. From all these findings we conclude that montelukast can prevent the decrease of IL-10 and inhibit the activation of NF-kappaB in inflammatory airway of asthmatic guinea pigs, which may be the new important mechanisms of montelukast's anti-airway-inflammation effects in asthmatic guinea pigs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".